{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9e2048",
   "metadata": {},
   "source": [
    "# Fine-Tuning ChatGPT for Different Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8ddc61",
   "metadata": {},
   "source": [
    "# Imports\n",
    "Here we import all the necessary packages and modules for our fine-tuning process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b410a579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pprint\n",
    "import time\n",
    "import argparse\n",
    "from ast import literal_eval\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "import wandb\n",
    "from openai import OpenAI\n",
    "import pandas as pd\n",
    "\n",
    "from utils_2 import (\n",
    "    dataset_has_format_errors,\n",
    "    check_token_statistics_and_cost_estimate,\n",
    "    load_dataset_task_prompt_mappings,\n",
    "    write_jsonl,\n",
    ")\n",
    "from utils_1 import task_num_to_task_name, dataset_num_to_dataset_name, plot_count_and_normalized_confusion_matrix, task_to_display_labels\n",
    "\n",
    "# Get the notebook's full path\n",
    "notebook_path = os.getcwd()\n",
    "\n",
    "# Set module_dir to the notebook's directory\n",
    "module_dir = notebook_path\n",
    "\n",
    "api_key_project = \"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\" #MK's Key\n",
    "#api_key_project = \"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\" #new PRODIGI key\n",
    "\n",
    "client = OpenAI(\n",
    "    api_key=api_key_project,  # This is the default and can be omitted\n",
    ")\n",
    "\n",
    "\n",
    "WANDB_PROJECT_NAME = \"chatgpt_annotations_llm_comparison\"\n",
    "MODEL_NAME = 'gpt-4o-mini-2024-07-18'\n",
    "COMPLETION_RETRIES = 50\n",
    "\n",
    "print(notebook_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772bcc76",
   "metadata": {},
   "source": [
    "# Utility Functions\n",
    "These functions provide various utilities for tasks such as data loading, token statistics, and other preprocessing steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7e1cb4",
   "metadata": {},
   "source": [
    "## Configuration Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c79142f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration Variables\n",
    "# Manually set the variables that were previously handled by argparse\n",
    "\n",
    "# Type of task to run inference on\n",
    "task = 1  # Choices: [1]\n",
    "\n",
    "# Dataset to run inference on\n",
    "dataset = 3  # Choices: [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "# Expert Dataset for Second Evaluation\n",
    "dataset_eval = 'x' # Choises: ['x']\n",
    "\n",
    "# Size of the sample to generate\n",
    "sample_size = '100'  # Choices: ['100','200']\n",
    "\n",
    "# Path to the directory to store the generated samples\n",
    "output_dir = '../../annotations/chatGPT/output_fine_tuning/'\n",
    "\n",
    "# Random seed to use\n",
    "seed = 2019\n",
    "\n",
    "# Path to the directory containing the datasets\n",
    "data_dir = '../../annotations/chatGPT/input_fine_tuning/'\n",
    "\n",
    "# Whether to use the full label\n",
    "not_use_full_labels = False\n",
    "\n",
    "# Path to the dataset-task mappings file\n",
    "dataset_task_mappings_fp = os.path.normpath(os.path.join(module_dir, '../../task_mappings', 'dataset_task_mappings.csv'))\n",
    "\n",
    "# Whether to rewrite the dataframe in OpenAI format\n",
    "rewrite_df_in_openai = True\n",
    "\n",
    "# Number of epochs to train the model\n",
    "n_epochs = 5\n",
    "\n",
    "# Batch Size for learning\n",
    "n_batch = 20\n",
    "\n",
    "# Name of the run\n",
    "run_name = 'finetune_chetGPT_hatespeech_appen'\n",
    "\n",
    "# Temperature to use when generating text\n",
    "temp = 0.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e81ba8",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ef6fa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_label_to_completion(label: str, task_num: int, full_label: bool = True) -> str:\n",
    "    new_label = ''\n",
    "\n",
    "    if task_num == 1:\n",
    "        if full_label:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'HATE SPEECH'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'TOXIC SPEECH'\n",
    "            else:\n",
    "                new_label = 'KEINE HATE SPEECH'\n",
    "            assert new_label in ['HATE SPEECH', 'TOXIC SPEECH', 'KEINE HATE SPEECH']\n",
    "        else:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'A'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'C'\n",
    "            else:\n",
    "                new_label = 'B'\n",
    "            assert new_label in ['A', 'B', 'C']\n",
    "\n",
    "    return new_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02978fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_and_eval_sets(data_dir: str, dataset_num: int, task_num: int, sample_size: int, dataset_eval:str) \\\n",
    "        -> dict[str, pd.DataFrame]:\n",
    "    datasets = dict()\n",
    "\n",
    "    train_dataset_task_files = glob.glob(os.path.join(data_dir, f'ds_{dataset_num}__task_{task_num}_train_set*.csv'))\n",
    "    eval_set_name = f'ds_{dataset_num}__task_{task_num}_eval_set'\n",
    "    datasets[eval_set_name] = pd.read_csv(os.path.join(data_dir, eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    # Load the additional evaluation dataset specified by dataset_eval\n",
    "    second_eval_set_name = f'ds_{dataset_eval}__task_{task_num}_full_eval'\n",
    "    datasets[second_eval_set_name] = pd.read_csv(os.path.join(data_dir, second_eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    if sample_size == 'all':\n",
    "        train_dfs_ = {fn.strip('.csv'): pd.read_csv(fn, encoding='utf-8') for fn in train_dataset_task_files}\n",
    "        datasets.update(train_dfs_)\n",
    "    else:\n",
    "        train_df_fn = f'ds_{dataset_num}__task_{task_num}_train_set_{sample_size}'\n",
    "        datasets[train_df_fn] = pd.read_csv(os.path.join(data_dir, train_df_fn + '.csv'), encoding='utf-8')\n",
    "\n",
    "        if train_df_fn not in [os.path.basename(fn).strip('.csv') for fn in train_dataset_task_files]:\n",
    "            raise ValueError(f\"Sample size {sample_size} not found for\"\n",
    "                             f\" dataset {dataset_num} and task {task_num}\")\n",
    "\n",
    "    return datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "904e16c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_training_example(system_prompt, user_prompt_format, user_prompt_text, completion):\n",
    "    return {'messages': [\n",
    "        {'role': 'system',\n",
    "         'content': system_prompt},\n",
    "\n",
    "        {'role': 'user',\n",
    "         'content': user_prompt_format.format(text=user_prompt_text)},\n",
    "\n",
    "        {'role': 'assistant',\n",
    "         'content': completion}\n",
    "    ]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bdf692a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets):\n",
    "    hatespeech_open_ai_metadata = list()\n",
    "\n",
    "    df_id_metadata = pd.DataFrame() if not os.path.exists('hatespeech_open_ai_metadata.csv') \\\n",
    "        else pd.read_csv('hatespeech_open_ai_metadata.csv')\n",
    "\n",
    "    for df_name, df in datasets.items():\n",
    "        df_jsonl_filename = os.path.join(output_dir, 'temp', df_name + '.jsonl')\n",
    "        write_jsonl(data_list=df['openai_instance_format'].tolist(), filename=df_jsonl_filename)\n",
    "\n",
    "        if not_use_full_labels:\n",
    "            df_name += '_single_letter_labels'\n",
    "\n",
    "        if (not rewrite_df_in_openai and\n",
    "                (len(df_id_metadata) > 0 and df_name in df_id_metadata['df_name'].tolist())):\n",
    "            print(f\"Dataset {df_name} already uploaded to OpenAI\")\n",
    "            continue\n",
    "\n",
    "        print(f\"Uploading {df_name} to OpenAI\")\n",
    "        df_response = client.files.create(\n",
    "            file=open(df_jsonl_filename, \"rb\"), purpose=\"fine-tune\"\n",
    "        )\n",
    "        df_response_dict = df_response.to_dict()\n",
    "        df_file_id = df_response_dict[\"id\"]\n",
    "\n",
    "        # Wait until the file is processed\n",
    "        while True:\n",
    "            file = client.files.retrieve(df_file_id)\n",
    "            file_dict = file.to_dict()\n",
    "            if file_dict[\"status\"] == \"processed\":\n",
    "                break\n",
    "            time.sleep(15)\n",
    "        hatespeech_open_ai_metadata.append({'df_name': df_name, 'file_id': df_file_id})\n",
    "\n",
    "    df_id_metadata = pd.concat([df_id_metadata, pd.DataFrame(hatespeech_open_ai_metadata)])\n",
    "    df_id_metadata.to_csv('hatespeech_open_ai_metadata.csv', index=False)\n",
    "\n",
    "    return df_id_metadata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ace921b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name, n_epochs):\n",
    "    response = client.fine_tuning.jobs.create(\n",
    "        training_file=training_file_id,\n",
    "        validation_file=evaluation_file_id,\n",
    "        model=\"gpt-4o-mini-2024-07-18\",\n",
    "        suffix=model_name,\n",
    "        hyperparameters={\"n_epochs\": n_epochs,\n",
    "                         \"batch_size\": n_batch}\n",
    "    )\n",
    "\n",
    "    response_dict = response.to_dict()\n",
    "    job_id = response_dict[\"id\"]\n",
    "    print(\"Job ID:\", response_dict[\"id\"])\n",
    "    print(\"Status:\", response_dict[\"status\"])\n",
    "\n",
    "    # Wait until the job is done\n",
    "    while True:\n",
    "        job = client.fine_tuning.jobs.retrieve(job_id)\n",
    "        job_dict = job.to_dict()\n",
    "        if job_dict[\"status\"] == \"succeeded\":\n",
    "            break\n",
    "        elif job_dict[\"status\"] == \"failed\":\n",
    "            raise Exception(\"Training failed: %s\" % job_dict[\"error\"])\n",
    "        time.sleep(30)\n",
    "\n",
    "    return job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2026f811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_and_log_finetuning_event_history(job_id):\n",
    "    response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id)\n",
    "    response_dict = response.to_dict()\n",
    "    events = response_dict[\"data\"]\n",
    "    events.reverse()\n",
    "    for event in events:\n",
    "        print(event[\"message\"])\n",
    "\n",
    "    # Log events\n",
    "    for event in events:\n",
    "        if event['type'] != 'metrics':\n",
    "            continue\n",
    "        data = event['data']\n",
    "        wandb.log(\n",
    "            {\n",
    "                \"train_loss\": data.get(\"train_loss\"),\n",
    "                \"valid_loss\": data.get(\"valid_loss\"),\n",
    "                \"train_mean_token_accuracy\": data.get(\"train_mean_token_accuracy\"),\n",
    "                \"valid_mean_token_accuracy\": data.get(\"valid_mean_token_accuracy\")\n",
    "            },\n",
    "            step=data.get('step', 0)\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc50a97",
   "metadata": {},
   "source": [
    "# Main Implementation\n",
    "This section contains the main code for fine-tuning the ChatGPT model. It includes setup, data preparation, training, and evaluation steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1b7246",
   "metadata": {},
   "source": [
    "### Connect to WandB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37564c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkublima\u001b[0m (\u001b[33mdigdemlab\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.19.7 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.15.11"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
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     "output_type": "display_data"
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    {
     "data": {
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       "Run data is saved locally in <code>e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\\wandb\\run-20250228_100228-6hiyo8kq</code>"
      ],
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "Syncing run <strong><a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/6hiyo8kq' target=\"_blank\">finetune_chetGPT_hatespeech_appen</a></strong> to <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/6hiyo8kq' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/6hiyo8kq</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/6hiyo8kq?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
      ],
      "text/plain": [
       "<wandb.sdk.wandb_run.Run at 0x20a84ff77c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 1/2\n",
    "# Initialize the Weights and Biases run\n",
    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=WANDB_PROJECT_NAME,\n",
    "    name=run_name if run_name != '' else f'{MODEL_NAME}_ds_{dataset}_task_{int(task)}'\n",
    "                                                    f'_sample_{sample_size}_epochs_{n_epochs}'\n",
    "                                                    f'_full_label_names_{str(not not_use_full_labels)}'\n",
    "                                                    f'_temp_{temp}',\n",
    "\n",
    "    # track hyperparameters and run metadata\n",
    "    config = {\n",
    "        \"model\": MODEL_NAME,\n",
    "        \"dataset\": dataset_num_to_dataset_name[int(dataset)],\n",
    "        \"task\": task_num_to_task_name[int(task)],\n",
    "        \"epochs\": n_epochs,\n",
    "        \"temp\": temp\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f1df25",
   "metadata": {},
   "source": [
    "### Load and Process Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2454b3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 3\n",
    "# Load the dataset and filename\n",
    "dataset_idx, dataset_task_mappings = load_dataset_task_prompt_mappings(\n",
    "    dataset_num=dataset, task_num=task, dataset_task_mappings_fp=dataset_task_mappings_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d35437fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 5\n",
    "# Load the train and eval datasets\n",
    "datasets = load_train_and_eval_sets(\n",
    "    data_dir=data_dir, dataset_num=dataset, task_num=task, sample_size=sample_size, dataset_eval=dataset_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f379906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact prompts>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 4\n",
    "# Get information specific to the dataset\n",
    "label_column = dataset_task_mappings.loc[dataset_idx, \"label_column\"]\n",
    "system_prompt = dataset_task_mappings.loc[dataset_idx, 'zero_shot_prompt']\n",
    "user_prompt_format = dataset_task_mappings.loc[dataset_idx, 'user_prompt']\n",
    "    \n",
    "# Log the system prompt and user_prompt_format as files in wandb\n",
    "prompts_artifact = wandb.Artifact('prompts', type='prompts')\n",
    "with prompts_artifact.new_file('system_prompt.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(system_prompt)\n",
    "with prompts_artifact.new_file('user_prompt_format.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(user_prompt_format)\n",
    "wandb.run.log_artifact(prompts_artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "47917349",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 6\n",
    "# Generate the training and evaluation examples in the way expected by the Open AI API to finetune chatgpt3.5\n",
    "preprocessed_output_dir = os.path.join(\n",
    "    output_dir, 'preprocessed', 'full_name_labels' if not not_use_full_labels else 'single_letter_labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a9616d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 7\n",
    "for df_name, df in datasets.items():\n",
    "    df['completion_label'] = df[label_column].map(\n",
    "        lambda label: map_label_to_completion(label=label, task_num = task,\n",
    "                                              full_label=not not_use_full_labels)\n",
    "        )\n",
    "    df['openai_instance_format'] = df.apply(\n",
    "        lambda row: create_training_example(\n",
    "            system_prompt=system_prompt, user_prompt_format=user_prompt_format,\n",
    "            user_prompt_text=row['text'],\n",
    "            completion=row['completion_label']\n",
    "        ),\n",
    "        axis=1\n",
    "    )\n",
    "    df['openai_instance_without_completion'] = df['openai_instance_format'].map(lambda x: x['messages'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bcd8c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check for errors ds_3__task_1_train_set_100 set: \n",
      "No errors found\n"
     ]
    }
   ],
   "source": [
    "# Section 8\n",
    "print(f'Check for errors {df_name} set: ')\n",
    "assert not dataset_has_format_errors(df['openai_instance_format'].tolist()), f\"Errors found in {df_name}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b1d7833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 9\n",
    "df.to_csv(os.path.join(preprocessed_output_dir, df_name + '.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cdc6b4",
   "metadata": {},
   "source": [
    "### Upload Training Dataset to OPEN AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2fd9b187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading ds_3__task_1_eval_set to OpenAI\n",
      "Uploading ds_x__task_1_full_eval to OpenAI\n",
      "Uploading ds_3__task_1_train_set_100 to OpenAI\n"
     ]
    }
   ],
   "source": [
    "# Section 10\n",
    "# Create jsonl file and upload to OpenAI\n",
    "df_id_metadata =upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "705758da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 11\n",
    "# delete all files in the temp folder\n",
    "os.system(f\"rm -rf {os.path.join(output_dir, 'temp')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4519ab5a",
   "metadata": {},
   "source": [
    "### Finetune chatGPT with the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0ec6e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 12\n",
    "# Run training on the train_df samples selected\n",
    "eval_set_name = f'ds_{dataset}__task_{task}_eval_set'\n",
    "eval_df = datasets[eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a76b1ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning ds_3__task_1_train_set_100\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Section 13\n",
    "print(f\"Finetuning {df_name}\")\n",
    "print('-' * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a1036bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 14\n",
    "# Log in wandb.config the dataset sample size used\n",
    "sample_size = df_name.split('_')[-1]\n",
    "wandb.config['trainset_size'] = sample_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0860e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the evaluation set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 999\n",
      "mean / median: 879.9213197969543, 869.0\n",
      "p5 / p95: 853.0, 921.4\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.324873096446701, 5.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~346689 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1733445 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 15\n",
    "# Check statistics and possible cost for each dataset\n",
    "print('Statistics for the evaluation set: ')\n",
    "eval_tokens = check_token_statistics_and_cost_estimate(\n",
    "    datasets[eval_set_name]['openai_instance_format'].tolist(), target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "254bdfe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the training set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 997\n",
      "mean / median: 879.38, 869.0\n",
      "p5 / p95: 853.0, 920.0\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.14, 5.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~87938 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~439690 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 16\n",
    "print(f'Statistics for the training set: ')\n",
    "train_tokens = check_token_statistics_and_cost_estimate(df['openai_instance_format'].tolist(),\n",
    "                                                        target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91aed7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tokens: 434627\n",
      "\n",
      "#### Estimated cost: 3.48 USD\n"
     ]
    }
   ],
   "source": [
    "# Section 17\n",
    "print(f\"Total number of tokens: {eval_tokens + train_tokens}\\n\")\n",
    "print(f\"#### Estimated cost: {round((eval_tokens + train_tokens) * (0.008 / 1000), 2)} USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a4cb776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 18\n",
    "# Run the fine-tuning job\n",
    "training_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == df_name, 'file_id'].values[0]\n",
    "evaluation_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == eval_set_name, 'file_id'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6b0baf56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: ftjob-Trbq7lcaHAR0CR5XifhL0mJM\n",
      "Status: validating_files\n"
     ]
    }
   ],
   "source": [
    "# Section 19\n",
    "model_name = (df_name.replace('__', '_')\n",
    "              .replace('train_set', 'trn')\n",
    "              .replace('task', 't')\n",
    "              .replace('_single_letter_labels', '_sl'))\n",
    "job_id = fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name=model_name, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6aff8caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model ft:gpt-4o-mini-2024-07-18:university-of-zurich-ipz-phd:ds-3-t-1-trn-100:B5qhwvMf has been successfully fine-tuned\n"
     ]
    }
   ],
   "source": [
    "# Section 20\n",
    "# Print the model name\n",
    "response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "response_dict = response.to_dict()\n",
    "full_model_name = response_dict[\"fine_tuned_model\"]\n",
    "print(f'The model {full_model_name} has been successfully fine-tuned')\n",
    "wandb.config['model_name_openai'] = full_model_name\n",
    "wandb.config['finetuning_jobid'] = job_id\n",
    "wandb.config['training_file_openai_id'] = response_dict['training_file']\n",
    "wandb.config['validation_file_openai_id'] = response_dict['validation_file']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "45b169bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 10/25: training loss=0.12, validation loss=0.23, full validation loss=0.15\n",
      "Step 11/25: training loss=0.10, validation loss=0.13\n",
      "Step 12/25: training loss=0.13, validation loss=0.14\n",
      "Step 13/25: training loss=0.18, validation loss=0.19\n",
      "Step 14/25: training loss=0.08, validation loss=0.15\n",
      "Step 15/25: training loss=0.15, validation loss=0.20, full validation loss=0.18\n",
      "Step 16/25: training loss=0.07, validation loss=0.23\n",
      "Step 17/25: training loss=0.09, validation loss=0.15\n",
      "Step 18/25: training loss=0.12, validation loss=0.18\n",
      "Step 19/25: training loss=0.09, validation loss=0.14\n",
      "Step 20/25: training loss=0.12, validation loss=0.13, full validation loss=0.19\n",
      "Step 21/25: training loss=0.13, validation loss=0.14\n",
      "Step 22/25: training loss=0.09, validation loss=0.18\n",
      "Step 23/25: training loss=0.11, validation loss=0.21\n",
      "Step 24/25: training loss=0.08, validation loss=0.17\n",
      "Step 25/25: training loss=0.10, validation loss=0.20, full validation loss=0.18\n",
      "Checkpoint created at step 15\n",
      "Checkpoint created at step 20\n",
      "New fine-tuned model created\n",
      "The job has successfully completed\n"
     ]
    }
   ],
   "source": [
    "# Section 21\n",
    "# Print the events (training history of the model)\n",
    "print_and_log_finetuning_event_history(job_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c579b37",
   "metadata": {},
   "source": [
    "## Evaluate Model on Validation Set with Annotations of the Group from which the Training Data is!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fba537b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Section 22\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0a544a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 394/394 [04:40<00:00,  1.40it/s]\n"
     ]
    }
   ],
   "source": [
    "# Section 23\n",
    "for messages in tqdm(eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "337b13ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 24\n",
    "# Add predictions to df\n",
    "eval_df['prediction'] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe242636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 25\n",
    "# Store output\n",
    "predictions_output_dir = os.path.join(output_dir, 'predictions',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir, exist_ok=True)\n",
    "datasets[eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bcaddde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 26\n",
    "# Get performance metrics\n",
    "y_true = eval_df['completion_label']\n",
    "y_pred = eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3d085eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 27\n",
    "label_type = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels = task_to_display_labels[task][label_type]\n",
    "labels = display_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a1ce1492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.25,\n",
      "                 'precision': 0.2777777777777778,\n",
      "                 'recall': 0.22727272727272727,\n",
      "                 'support': 66},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.5848563968668407,\n",
      "                       'precision': 0.5925925925925926,\n",
      "                       'recall': 0.5773195876288659,\n",
      "                       'support': 194},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.34385964912280703,\n",
      "                  'precision': 0.32450331125827814,\n",
      "                  'recall': 0.3656716417910448,\n",
      "                  'support': 134},\n",
      " 'accuracy': 0.4467005076142132,\n",
      " 'macro avg': {'f1-score': 0.3929053486632159,\n",
      "               'precision': 0.3982912272095495,\n",
      "               'recall': 0.3900879855642127,\n",
      "               'support': 394},\n",
      " 'weighted avg': {'f1-score': 0.4468003400371148,\n",
      "                  'precision': 0.44867954315965886,\n",
      "                  'recall': 0.4467005076142132,\n",
      "                  'support': 394}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 28\n",
    "cm_plot, classification_report, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true, y_pred, display_labels, labels, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "75c64dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 29\n",
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4aaa4209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 31\n",
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "45aaca05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_ds_3_t_1_trn_100>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 32\n",
    "# Log the classification report as an artifact\n",
    "classification_report = (pd.DataFrame({k: v for k, v in classification_report.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report': wandb.Table(\n",
    "    dataframe=classification_report)})\n",
    "\n",
    "classification_report_artifact = wandb.Artifact(\n",
    "      f'classification_report_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact.new_file('classification_report.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db7bbf2",
   "metadata": {},
   "source": [
    "## Evaluation on Second Evaluation Set\n",
    "In this section, we will evaluate the performance of our model on a second evaluation set (`second_eval_set`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8cbb7ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['ds_3__task_1_eval_set', 'ds_x__task_1_full_eval', 'ds_3__task_1_train_set_100'])\n"
     ]
    }
   ],
   "source": [
    "print(datasets.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_eval_set_name = f'ds_{dataset_eval}__task_{task}_full_eval'\n",
    "full_eval_df = datasets[second_eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == second_eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9cfc903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_eval_df = datasets[second_eval_set_name]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "303aeed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Load the second evaluation set (second_eval_set)\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions_2 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7c218926",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 494/494 [05:17<00:00,  1.56it/s]\n"
     ]
    }
   ],
   "source": [
    "for messages in tqdm(full_eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions_2.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "789fce14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add predictions to df\n",
    "full_eval_df['prediction'] = predictions_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "616c4658",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_output_dir_2 = os.path.join(output_dir, 'predictions_2',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir_2, exist_ok=True)\n",
    "datasets[second_eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir_2, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ec8481ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get performance metrics\n",
    "y_true_2 = full_eval_df['completion_label']\n",
    "y_pred_2 = full_eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "296deb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_type_2 = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels_2 = task_to_display_labels[task][label_type]\n",
    "labels_2 = display_labels_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "83dbd847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.38392857142857145,\n",
      "                 'precision': 0.5375,\n",
      "                 'recall': 0.2986111111111111,\n",
      "                 'support': 144},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.7226890756302521,\n",
      "                       'precision': 0.7818181818181819,\n",
      "                       'recall': 0.671875,\n",
      "                       'support': 256},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.3194444444444444,\n",
      "                  'precision': 0.23711340206185566,\n",
      "                  'recall': 0.48936170212765956,\n",
      "                  'support': 94},\n",
      " 'accuracy': 0.52834008097166,\n",
      " 'macro avg': {'f1-score': 0.4753540305010893,\n",
      "               'precision': 0.5188105279600125,\n",
      "               'recall': 0.4866159377462569,\n",
      "               'support': 494},\n",
      " 'weighted avg': {'f1-score': 0.5472103146251753,\n",
      "                  'precision': 0.6069516484600587,\n",
      "                  'recall': 0.52834008097166,\n",
      "                  'support': 494}}\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_plot, classification_report_2, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true_2, y_pred_2, display_labels_2, labels_2, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "20289ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "09a5abfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix_2': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2399e29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_2_ds_3_t_1_trn_100>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log the classification report as an artifact\n",
    "classification_report_2 = (pd.DataFrame({k: v for k, v in classification_report_2.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report_2': wandb.Table(\n",
    "    dataframe=classification_report_2)})\n",
    "\n",
    "classification_report_artifact_2 = wandb.Artifact(\n",
    "      f'classification_report_2_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact_2.new_file('classification_report_2.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report_2))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b0aa2d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
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